{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our special classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priors classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class laplace_prior(object):\n",
    "    def __init__(self, mu, b):\n",
    "        self.mu = mu\n",
    "        self.b = b\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        if do_sum:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b).sum()\n",
    "        else:\n",
    "            return (-np.log(2*self.b) - torch.abs(x - self.mu)/self.b)\n",
    "\n",
    "class isotropic_gauss_prior(object):\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "        \n",
    "        self.cte_term = -(0.5)*np.log(2*np.pi)\n",
    "        self.det_sig_term = -np.log(self.sigma)\n",
    "\n",
    "    def loglike(self, x, do_sum=True):\n",
    "        \n",
    "        dist_term = -(0.5)*((x - self.mu)/self.sigma)**2\n",
    "        if do_sum:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term).sum()\n",
    "        else:\n",
    "            return (self.cte_term + self.det_sig_term + dist_term)\n",
    "    \n",
    "\n",
    "# TODO: adapt so can be done without sum\n",
    "class spike_slab_2GMM(object):\n",
    "    def __init__(self, mu1, mu2, sigma1, sigma2, pi):\n",
    "        \n",
    "        self.N1 = isotropic_gauss_prior(mu1, sigma1)\n",
    "        self.N2 = isotropic_gauss_prior(mu2, sigma2)\n",
    "        \n",
    "        self.pi1 = pi \n",
    "        self.pi2 = (1-pi)\n",
    "\n",
    "    def loglike(self, x):\n",
    "        \n",
    "        N1_ll = self.N1.loglike(x)\n",
    "        N2_ll = self.N2.loglike(x)\n",
    "        \n",
    "        # Numerical stability trick -> unnormalising logprobs will underflow otherwise\n",
    "        max_loglike = torch.max(N1_ll, N2_ll)\n",
    "        normalised_like = self.pi1 + torch.exp(N1_ll - max_loglike) + self.pi2 + torch.exp(N2_ll - max_loglike)\n",
    "        loglike = torch.log(normalised_like) + max_loglike\n",
    "    \n",
    "        return loglike\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isnan(tensor):\n",
    "  # Gross: https://github.com/pytorch/pytorch/issues/4767\n",
    "    return (tensor != tensor)\n",
    "\n",
    "def hasnan(tensor):\n",
    "    return isnan(tensor).any()\n",
    "\n",
    "def isotropic_gauss_loglike(x, mu, sigma, do_sum=True):\n",
    "    cte_term = -(0.5)*np.log(2*np.pi)\n",
    "    det_sig_term = -torch.log(sigma)\n",
    "    inner = (x - mu)/sigma\n",
    "    dist_term = -(0.5)*(inner**2)\n",
    "    \n",
    "    if do_sum:\n",
    "        out = (cte_term + det_sig_term + dist_term).sum() # sum over all weights\n",
    "    else:\n",
    "        out = (cte_term + det_sig_term + dist_term)\n",
    "#     print(hasnan(x.data), hasnan(mu.data), hasnan(sigma.data), out)\n",
    "#     if isnan(out) or hasnan(out):\n",
    "#         print('NAaaanNNN')\n",
    "#         print('mu max', mu.max())\n",
    "#         print('mu min', mu.min())\n",
    "#         print('sigma max', sigma.max())\n",
    "#         print('sigma min', sigma.min())\n",
    "#         print('x max', x.max())\n",
    "#         print('x min', x.min())\n",
    "#         print((x - mu).max())\n",
    "#         print((x - mu).min())\n",
    "#         print(hasnan(inner))\n",
    "#         print(dist_term)\n",
    "#         print(hasnan(dist_term))\n",
    "    return out \n",
    "\n",
    "\n",
    "\n",
    "class BayesLinear_Normalq(nn.Module):\n",
    "    def __init__(self, n_in, n_out, prior_class):\n",
    "        super(BayesLinear_Normalq, self).__init__()\n",
    "        self.n_in = n_in\n",
    "        self.n_out = n_out\n",
    "        self.prior = prior_class\n",
    "        \n",
    "        # Learnable parameters\n",
    "        self.W_mu = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.W_p = nn.Parameter(torch.Tensor(self.n_in, self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "        self.b_mu = nn.Parameter(torch.Tensor(self.n_out).uniform_(-0.2, 0.2))\n",
    "        self.b_p = nn.Parameter(torch.Tensor(self.n_out).uniform_(-3, -2))\n",
    "        \n",
    "       \n",
    "        self.lpw = 0\n",
    "        self.lqw = 0\n",
    "        \n",
    "                                   \n",
    "    def forward(self, X, sample=False):\n",
    "#         print(self.training)\n",
    "\n",
    "        if not self.training and not sample: # This is just a placeholder function\n",
    "            output = torch.mm(X, self.W_mu) + self.b_mu.expand(X.size()[0], self.n_out)\n",
    "            return output, 0, 0\n",
    "                                       \n",
    "        else:\n",
    "                              \n",
    "            # Tensor.new()  Constructs a new tensor of the same data type as self tensor. \n",
    "            # the same random sample is used for every element in the minibatch\n",
    "            eps_W = Variable(self.W_mu.data.new(self.W_mu.size()).normal_())\n",
    "            eps_b = Variable(self.b_mu.data.new(self.b_mu.size()).normal_())\n",
    "                                       \n",
    "            # sample parameters         \n",
    "            \n",
    "            std_w = 1e-6 + F.softplus(self.W_p, beta=1, threshold=20)\n",
    "            std_b = 1e-6 + F.softplus(self.b_p, beta=1, threshold=20)                      \n",
    "                                   \n",
    "            W = self.W_mu + 1 * std_w * eps_W\n",
    "            b = self.b_mu + 1 * std_b * eps_b          \n",
    "    \n",
    "            \n",
    "            output = torch.mm(X, W) + b.unsqueeze(0).expand(X.shape[0], -1) # (batch_size, n_output)\n",
    "            \n",
    "            lqw = isotropic_gauss_loglike(W, self.W_mu, std_w) + isotropic_gauss_loglike(b, self.b_mu, std_b)\n",
    "            lpw = self.prior.loglike(W) + self.prior.loglike(b)\n",
    "            return output, lqw, lpw\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick weight sampling function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_weights(W_mu, b_mu, W_p, b_p):\n",
    "    \n",
    "    eps_W = W_mu.data.new(W_mu.size()).normal_()\n",
    "    # sample parameters         \n",
    "    std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)     \n",
    "    W = W_mu + 1 * std_w * eps_W\n",
    "    \n",
    "    if b_mu is not None:\n",
    "        std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)  \n",
    "        eps_b = b_mu.data.new(b_mu.size()).normal_()\n",
    "        b = b_mu + 1 * std_b * eps_b\n",
    "    else:\n",
    "        b = None\n",
    "    \n",
    "    return W, b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class bayes_linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(bayes_linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "#         prior_instance = isotropic_gauss_prior(mu=0, sigma=0.1)\n",
    "#         prior_instance = spike_slab_2GMM(mu1=0, mu2=0, sigma1=0.135, sigma2=0.001, pi=0.5)\n",
    "        self.prior_instance = laplace_prior(mu=0, b=0.1)\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.bfc1 = BayesLinear_Normalq(input_dim, n_hid, self.prior_instance)\n",
    "        self.bfc2 = BayesLinear_Normalq(n_hid, n_hid, self.prior_instance)\n",
    "        self.bfc3 = BayesLinear_Normalq(n_hid, output_dim, self.prior_instance)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x, sample=False):\n",
    "        \n",
    "        tlqw = 0\n",
    "        tlpw = 0\n",
    "        \n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc1(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x, lqw, lpw = self.bfc2(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y, lqw, lpw = self.bfc3(x, sample)\n",
    "        tlqw = tlqw + lqw\n",
    "        tlpw = tlpw + lpw\n",
    "        \n",
    "        return y, tlqw, tlpw\n",
    "    \n",
    "    def sample_predict(self, x, Nsamples):\n",
    "        \n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        tlqw_vec = np.zeros(Nsamples)\n",
    "        tlpw_vec = np.zeros(Nsamples)\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            \n",
    "            y, tlqw, tlpw = self.forward(x, sample=True)\n",
    "            predictions[i] = y\n",
    "            tlqw_vec[i] = tlqw\n",
    "            tlpw_vec[i] = tlpw \n",
    "            \n",
    "        return predictions, tlqw_vec, tlpw_vec\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import copy\n",
    "\n",
    "class Bayes_Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, Nbatches=0):\n",
    "        super(Bayes_Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.Nbatches = Nbatches\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = bayes_linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y, samples=1):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        if samples == 1:\n",
    "            out, tlqw, tlpw = self.model(x)\n",
    "            mlpdw = F.cross_entropy(out, y, reduction='sum')\n",
    "            Edkl = (tlqw - tlpw)/self.Nbatches\n",
    "            \n",
    "        elif samples > 1:\n",
    "            mlpdw_cum = 0\n",
    "            Edkl_cum = 0\n",
    "            \n",
    "            for i in range(samples):\n",
    "                out, tlqw, tlpw = self.model(x, sample=True)\n",
    "                mlpdw_i = F.cross_entropy(out, y, reduction='sum')\n",
    "                Edkl_i = (tlqw - tlpw)/self.Nbatches\n",
    "                mlpdw_cum = mlpdw_cum + mlpdw_i\n",
    "                Edkl_cum = Edkl_cum + Edkl_i \n",
    "            \n",
    "            mlpdw = mlpdw_cum/samples\n",
    "            Edkl = Edkl_cum/samples\n",
    "        \n",
    "        loss = Edkl + mlpdw\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return Edkl.data, mlpdw.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def sample_eval(self, x, y, Nsamples, logits=True, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out, _, _ = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    \n",
    "    def get_weight_samples(self, Nsamples=10):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_vec = []\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            previous_layer_name = ''\n",
    "            for key in state_dict.keys():\n",
    "                layer_name = key.split('.')[0]\n",
    "                if layer_name != previous_layer_name:\n",
    "                    previous_layer_name = layer_name\n",
    "\n",
    "                    W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                    W_p = state_dict[layer_name+'.W_p'].data\n",
    "\n",
    "    #                 b_mu = state_dict[layer_name+'.b_mu'].cpu().data\n",
    "    #                 b_p = state_dict[layer_name+'.b_p'].cpu().data\n",
    "\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=None, W_p=W_p, b_p=None)\n",
    "\n",
    "                    for weight in W.cpu().view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)\n",
    "    \n",
    "    def get_weight_SNR(self, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_SNR_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                sig_W = 1e-6 + F.softplus(W_p, beta=1, threshold=20)\n",
    "\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                sig_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                W_snr = (torch.abs(W_mu)/sig_W)\n",
    "                b_snr = (torch.abs(b_mu)/sig_b)\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = W_snr > thresh\n",
    "                    mask_dict[layer_name+'.b'] = b_snr > thresh\n",
    "                    \n",
    "                else:\n",
    "                \n",
    "                    for weight_SNR in W_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "\n",
    "                    for weight_SNR in b_snr.cpu().view(-1):\n",
    "                        weight_SNR_vec.append(weight_SNR)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:\n",
    "            return np.array(weight_SNR_vec)\n",
    "    \n",
    "    \n",
    "    def get_weight_KLD(self, Nsamples=20, thresh=None):\n",
    "        state_dict = self.model.state_dict()\n",
    "        weight_KLD_vec = []\n",
    "        \n",
    "        if thresh is not None:\n",
    "            mask_dict = {}\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "\n",
    "                W_mu = state_dict[layer_name+'.W_mu'].data\n",
    "                W_p = state_dict[layer_name+'.W_p'].data\n",
    "                b_mu = state_dict[layer_name+'.b_mu'].data\n",
    "                b_p = state_dict[layer_name+'.b_p'].data\n",
    "                \n",
    "                std_w = 1e-6 + F.softplus(W_p, beta=1, threshold=20)  \n",
    "                std_b = 1e-6 + F.softplus(b_p, beta=1, threshold=20)\n",
    "\n",
    "                KL_W = W_mu.new(W_mu.size()).zero_()\n",
    "                KL_b = b_mu.new(b_mu.size()).zero_()\n",
    "                for i in range(Nsamples):\n",
    "                    W, b = sample_weights(W_mu=W_mu, b_mu=b_mu, W_p=W_p, b_p=b_p)  \n",
    "                    # Note that this will currently not work with slab and spike prior\n",
    "                    KL_W += isotropic_gauss_loglike(W, W_mu, std_w, do_sum=False) - self.model.prior_instance.loglike(W, do_sum=False)\n",
    "                    KL_b += isotropic_gauss_loglike(b, b_mu, std_b, do_sum=False) - self.model.prior_instance.loglike(b, do_sum=False)\n",
    "\n",
    "                KL_W /= Nsamples\n",
    "                KL_b /= Nsamples\n",
    "                \n",
    "                if thresh is not None:\n",
    "                    mask_dict[layer_name+'.W'] = KL_W > thresh\n",
    "                    mask_dict[layer_name+'.b'] = KL_b > thresh\n",
    "                    \n",
    "                else:\n",
    "\n",
    "                    for weight_KLD in KL_W.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "\n",
    "                    for weight_KLD in KL_b.cpu().view(-1):\n",
    "                        weight_KLD_vec.append(weight_KLD)\n",
    "        \n",
    "        if thresh is not None:\n",
    "            return mask_dict\n",
    "        else:    \n",
    "            return np.array(weight_KLD_vec)\n",
    "        \n",
    "        \n",
    "    def mask_model(self, Nsamples=0, thresh=0):\n",
    "        '''\n",
    "        Nsamples is used to select SNR (0) or KLD (>0) based masking\n",
    "        '''\n",
    "        original_state_dict = copy.deepcopy(self.model.state_dict())\n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        if Nsamples == 0:\n",
    "            mask_dict = self.get_weight_SNR(thresh=thresh)\n",
    "        else:\n",
    "            mask_dict = self.get_weight_KLD(Nsamples=Nsamples, thresh=thresh)\n",
    "        \n",
    "        n_unmasked = 0\n",
    "        \n",
    "        previous_layer_name = ''\n",
    "        for key in state_dict.keys():\n",
    "            layer_name = key.split('.')[0]\n",
    "            if layer_name != previous_layer_name:\n",
    "                previous_layer_name = layer_name\n",
    "                \n",
    "                state_dict[layer_name+'.W_mu'][1-mask_dict[layer_name+'.W']] = 0\n",
    "                state_dict[layer_name+'.W_p'][1-mask_dict[layer_name+'.W']] = -1000\n",
    "                \n",
    "                state_dict[layer_name+'.b_mu'][1-mask_dict[layer_name+'.b']] = 0\n",
    "                state_dict[layer_name+'.b_p'][1-mask_dict[layer_name+'.b']] = -1000\n",
    "                \n",
    "                \n",
    "                n_unmasked += mask_dict[layer_name+'.W'].sum()\n",
    "                n_unmasked += mask_dict[layer_name+'.b'].sum()\n",
    "                \n",
    "        return original_state_dict, n_unmasked\n",
    "            \n",
    "            \n",
    "            \n",
    "        \n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(net.model.state_dict()['bfc1.W_mu'].cpu().data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import copy\n",
    "# aaa = copy.deepcopy(net.model)\n",
    "# net.model = aaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/140, Jtr_KL = 27.501154, Jtr_pred = 5.047559, err = 0.208167, \u001b[31m   time: 41.441758 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.305804, err = 0.085100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1/140, Jtr_KL = 25.433438, Jtr_pred = 0.429577, err = 0.118000, \u001b[31m   time: 41.435449 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.220301, err = 0.063400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 2/140, Jtr_KL = 23.486512, Jtr_pred = 0.348816, err = 0.099200, \u001b[31m   time: 41.786939 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.199058, err = 0.058600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 3/140, Jtr_KL = 21.669902, Jtr_pred = 0.307000, err = 0.086833, \u001b[31m   time: 41.557091 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169844, err = 0.048300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 4/140, Jtr_KL = 19.984696, Jtr_pred = 0.278407, err = 0.079583, \u001b[31m   time: 41.714683 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151858, err = 0.045100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 5/140, Jtr_KL = 18.430667, Jtr_pred = 0.262886, err = 0.075350, \u001b[31m   time: 41.701128 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.146768, err = 0.043800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 6/140, Jtr_KL = 17.009743, Jtr_pred = 0.248728, err = 0.071067, \u001b[31m   time: 41.624264 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.145668, err = 0.042000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 7/140, Jtr_KL = 15.718240, Jtr_pred = 0.241878, err = 0.068100, \u001b[31m   time: 41.677124 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131944, err = 0.039900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 8/140, Jtr_KL = 14.552343, Jtr_pred = 0.234083, err = 0.065950, \u001b[31m   time: 41.551106 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140028, err = 0.041600\n",
      "\u001b[0m\n",
      "it 9/140, Jtr_KL = 13.509824, Jtr_pred = 0.231446, err = 0.064933, \u001b[31m   time: 41.451143 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131774, err = 0.038000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 10/140, Jtr_KL = 12.583929, Jtr_pred = 0.227901, err = 0.064750, \u001b[31m   time: 41.304800 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.130538, err = 0.038600\n",
      "\u001b[0m\n",
      "it 11/140, Jtr_KL = 11.766689, Jtr_pred = 0.222336, err = 0.063817, \u001b[31m   time: 41.241809 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.127637, err = 0.038400\n",
      "\u001b[0m\n",
      "it 12/140, Jtr_KL = 11.052097, Jtr_pred = 0.220085, err = 0.063100, \u001b[31m   time: 41.240396 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.121285, err = 0.036000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 13/140, Jtr_KL = 10.430735, Jtr_pred = 0.218660, err = 0.061933, \u001b[31m   time: 41.711236 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.117459, err = 0.034300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 14/140, Jtr_KL = 9.893553, Jtr_pred = 0.214179, err = 0.060350, \u001b[31m   time: 41.681783 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.120505, err = 0.033700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 15/140, Jtr_KL = 9.432761, Jtr_pred = 0.216390, err = 0.061783, \u001b[31m   time: 41.698164 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.121617, err = 0.035200\n",
      "\u001b[0m\n",
      "it 16/140, Jtr_KL = 9.038303, Jtr_pred = 0.213838, err = 0.059933, \u001b[31m   time: 41.690053 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111663, err = 0.032400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 17/140, Jtr_KL = 8.703045, Jtr_pred = 0.211442, err = 0.059950, \u001b[31m   time: 41.916994 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.111722, err = 0.032000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 18/140, Jtr_KL = 8.416996, Jtr_pred = 0.212951, err = 0.058683, \u001b[31m   time: 41.640117 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.123850, err = 0.034800\n",
      "\u001b[0m\n",
      "it 19/140, Jtr_KL = 8.175065, Jtr_pred = 0.207365, err = 0.057417, \u001b[31m   time: 41.642959 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108962, err = 0.030300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 20/140, Jtr_KL = 7.968828, Jtr_pred = 0.206064, err = 0.056400, \u001b[31m   time: 41.597376 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.108657, err = 0.031300\n",
      "\u001b[0m\n",
      "it 21/140, Jtr_KL = 7.794894, Jtr_pred = 0.202940, err = 0.055800, \u001b[31m   time: 41.593421 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.101842, err = 0.028000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 22/140, Jtr_KL = 7.646734, Jtr_pred = 0.206992, err = 0.057283, \u001b[31m   time: 41.610704 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.105702, err = 0.030500\n",
      "\u001b[0m\n",
      "it 23/140, Jtr_KL = 7.520568, Jtr_pred = 0.199217, err = 0.054783, \u001b[31m   time: 41.699008 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.107022, err = 0.029600\n",
      "\u001b[0m\n",
      "it 24/140, Jtr_KL = 7.412752, Jtr_pred = 0.197685, err = 0.054817, \u001b[31m   time: 41.755521 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.109034, err = 0.031500\n",
      "\u001b[0m\n",
      "it 25/140, Jtr_KL = 7.321220, Jtr_pred = 0.197383, err = 0.054033, \u001b[31m   time: 41.692100 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.104957, err = 0.029500\n",
      "\u001b[0m\n",
      "it 26/140, Jtr_KL = 7.242401, Jtr_pred = 0.191573, err = 0.052283, \u001b[31m   time: 41.658534 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099583, err = 0.029400\n",
      "\u001b[0m\n",
      "it 27/140, Jtr_KL = 7.174140, Jtr_pred = 0.191522, err = 0.051750, \u001b[31m   time: 41.474872 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096482, err = 0.027700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 28/140, Jtr_KL = 7.114893, Jtr_pred = 0.191135, err = 0.051750, \u001b[31m   time: 41.626396 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.098588, err = 0.026800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 29/140, Jtr_KL = 7.064191, Jtr_pred = 0.186391, err = 0.049800, \u001b[31m   time: 41.582132 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.095577, err = 0.027800\n",
      "\u001b[0m\n",
      "it 30/140, Jtr_KL = 7.019420, Jtr_pred = 0.182556, err = 0.050900, \u001b[31m   time: 41.754542 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.099487, err = 0.028200\n",
      "\u001b[0m\n",
      "it 31/140, Jtr_KL = 6.979145, Jtr_pred = 0.184164, err = 0.051067, \u001b[31m   time: 41.695181 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094512, err = 0.026500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 32/140, Jtr_KL = 6.944694, Jtr_pred = 0.179039, err = 0.050233, \u001b[31m   time: 41.727797 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.094080, err = 0.027400\n",
      "\u001b[0m\n",
      "it 33/140, Jtr_KL = 6.913033, Jtr_pred = 0.181781, err = 0.050300, \u001b[31m   time: 41.670948 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096518, err = 0.028800\n",
      "\u001b[0m\n",
      "it 34/140, Jtr_KL = 6.886128, Jtr_pred = 0.179107, err = 0.049117, \u001b[31m   time: 41.716220 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093521, err = 0.026600\n",
      "\u001b[0m\n",
      "it 35/140, Jtr_KL = 6.860731, Jtr_pred = 0.178363, err = 0.049783, \u001b[31m   time: 41.706941 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.097043, err = 0.027000\n",
      "\u001b[0m\n",
      "it 36/140, Jtr_KL = 6.838630, Jtr_pred = 0.173875, err = 0.049383, \u001b[31m   time: 41.696985 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088562, err = 0.023500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 37/140, Jtr_KL = 6.817895, Jtr_pred = 0.174697, err = 0.048483, \u001b[31m   time: 41.574126 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.096905, err = 0.026300\n",
      "\u001b[0m\n",
      "it 38/140, Jtr_KL = 6.799320, Jtr_pred = 0.173175, err = 0.048817, \u001b[31m   time: 41.829932 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092201, err = 0.025700\n",
      "\u001b[0m\n",
      "it 39/140, Jtr_KL = 6.781909, Jtr_pred = 0.170745, err = 0.047650, \u001b[31m   time: 41.762960 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090675, err = 0.026300\n",
      "\u001b[0m\n",
      "it 40/140, Jtr_KL = 6.765702, Jtr_pred = 0.170979, err = 0.046550, \u001b[31m   time: 41.672468 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091670, err = 0.025400\n",
      "\u001b[0m\n",
      "it 41/140, Jtr_KL = 6.751049, Jtr_pred = 0.165754, err = 0.045900, \u001b[31m   time: 41.744261 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092324, err = 0.025600\n",
      "\u001b[0m\n",
      "it 42/140, Jtr_KL = 6.736751, Jtr_pred = 0.169386, err = 0.047650, \u001b[31m   time: 41.744682 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088916, err = 0.025500\n",
      "\u001b[0m\n",
      "it 43/140, Jtr_KL = 6.723429, Jtr_pred = 0.166442, err = 0.045067, \u001b[31m   time: 41.820064 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092302, err = 0.026100\n",
      "\u001b[0m\n",
      "it 44/140, Jtr_KL = 6.711112, Jtr_pred = 0.166248, err = 0.046583, \u001b[31m   time: 41.758088 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090320, err = 0.026800\n",
      "\u001b[0m\n",
      "it 45/140, Jtr_KL = 6.699355, Jtr_pred = 0.166868, err = 0.046400, \u001b[31m   time: 41.760420 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092353, err = 0.025700\n",
      "\u001b[0m\n",
      "it 46/140, Jtr_KL = 6.688516, Jtr_pred = 0.166317, err = 0.045850, \u001b[31m   time: 41.758922 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.092884, err = 0.025200\n",
      "\u001b[0m\n",
      "it 47/140, Jtr_KL = 6.678121, Jtr_pred = 0.163786, err = 0.045650, \u001b[31m   time: 41.692331 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091209, err = 0.025500\n",
      "\u001b[0m\n",
      "it 48/140, Jtr_KL = 6.668306, Jtr_pred = 0.159672, err = 0.045767, \u001b[31m   time: 41.629222 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081756, err = 0.023200\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 49/140, Jtr_KL = 6.657679, Jtr_pred = 0.158412, err = 0.044267, \u001b[31m   time: 41.579288 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.093760, err = 0.025700\n",
      "\u001b[0m\n",
      "it 50/140, Jtr_KL = 6.648251, Jtr_pred = 0.157600, err = 0.044400, \u001b[31m   time: 41.504513 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090315, err = 0.025300\n",
      "\u001b[0m\n",
      "it 51/140, Jtr_KL = 6.638481, Jtr_pred = 0.157569, err = 0.043717, \u001b[31m   time: 41.530359 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089423, err = 0.025000\n",
      "\u001b[0m\n",
      "it 52/140, Jtr_KL = 6.629743, Jtr_pred = 0.157804, err = 0.043800, \u001b[31m   time: 41.561505 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089540, err = 0.024300\n",
      "\u001b[0m\n",
      "it 53/140, Jtr_KL = 6.620040, Jtr_pred = 0.156156, err = 0.043733, \u001b[31m   time: 41.545833 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090989, err = 0.025100\n",
      "\u001b[0m\n",
      "it 54/140, Jtr_KL = 6.612018, Jtr_pred = 0.155844, err = 0.045083, \u001b[31m   time: 41.600461 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085379, err = 0.024000\n",
      "\u001b[0m\n",
      "it 55/140, Jtr_KL = 6.603796, Jtr_pred = 0.155032, err = 0.044183, \u001b[31m   time: 41.615351 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085830, err = 0.023700\n",
      "\u001b[0m\n",
      "it 56/140, Jtr_KL = 6.595569, Jtr_pred = 0.151748, err = 0.041633, \u001b[31m   time: 41.599143 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083502, err = 0.022900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 57/140, Jtr_KL = 6.587004, Jtr_pred = 0.154759, err = 0.043617, \u001b[31m   time: 41.504672 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085032, err = 0.022400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 58/140, Jtr_KL = 6.579131, Jtr_pred = 0.149906, err = 0.043083, \u001b[31m   time: 41.488801 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088636, err = 0.025100\n",
      "\u001b[0m\n",
      "it 59/140, Jtr_KL = 6.571358, Jtr_pred = 0.149921, err = 0.041950, \u001b[31m   time: 41.503190 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085729, err = 0.023400\n",
      "\u001b[0m\n",
      "it 60/140, Jtr_KL = 6.563254, Jtr_pred = 0.150813, err = 0.041717, \u001b[31m   time: 41.491940 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083004, err = 0.023800\n",
      "\u001b[0m\n",
      "it 61/140, Jtr_KL = 6.555731, Jtr_pred = 0.151742, err = 0.042250, \u001b[31m   time: 41.451125 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081471, err = 0.023400\n",
      "\u001b[0m\n",
      "it 62/140, Jtr_KL = 6.548511, Jtr_pred = 0.149940, err = 0.041883, \u001b[31m   time: 41.466851 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083913, err = 0.023300\n",
      "\u001b[0m\n",
      "it 63/140, Jtr_KL = 6.541407, Jtr_pred = 0.150782, err = 0.042250, \u001b[31m   time: 41.434137 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081468, err = 0.023100\n",
      "\u001b[0m\n",
      "it 64/140, Jtr_KL = 6.533905, Jtr_pred = 0.149032, err = 0.042333, \u001b[31m   time: 41.473733 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080474, err = 0.024600\n",
      "\u001b[0m\n",
      "it 65/140, Jtr_KL = 6.527016, Jtr_pred = 0.148270, err = 0.042917, \u001b[31m   time: 41.476979 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083300, err = 0.023500\n",
      "\u001b[0m\n",
      "it 66/140, Jtr_KL = 6.519307, Jtr_pred = 0.147086, err = 0.040500, \u001b[31m   time: 41.441125 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084600, err = 0.024100\n",
      "\u001b[0m\n",
      "it 67/140, Jtr_KL = 6.512103, Jtr_pred = 0.146308, err = 0.041500, \u001b[31m   time: 41.434362 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084563, err = 0.023700\n",
      "\u001b[0m\n",
      "it 68/140, Jtr_KL = 6.505601, Jtr_pred = 0.145666, err = 0.041217, \u001b[31m   time: 41.491679 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084105, err = 0.023300\n",
      "\u001b[0m\n",
      "it 69/140, Jtr_KL = 6.498424, Jtr_pred = 0.144849, err = 0.041417, \u001b[31m   time: 41.460207 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083690, err = 0.023800\n",
      "\u001b[0m\n",
      "it 70/140, Jtr_KL = 6.491435, Jtr_pred = 0.145090, err = 0.040483, \u001b[31m   time: 41.456879 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082876, err = 0.024100\n",
      "\u001b[0m\n",
      "it 71/140, Jtr_KL = 6.484233, Jtr_pred = 0.143437, err = 0.041133, \u001b[31m   time: 41.486739 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089748, err = 0.023600\n",
      "\u001b[0m\n",
      "it 72/140, Jtr_KL = 6.477385, Jtr_pred = 0.143997, err = 0.038817, \u001b[31m   time: 41.408407 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081879, err = 0.022600\n",
      "\u001b[0m\n",
      "it 73/140, Jtr_KL = 6.470333, Jtr_pred = 0.143550, err = 0.041283, \u001b[31m   time: 40.993944 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081683, err = 0.022800\n",
      "\u001b[0m\n",
      "it 74/140, Jtr_KL = 6.463703, Jtr_pred = 0.144322, err = 0.041517, \u001b[31m   time: 41.446771 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088393, err = 0.025900\n",
      "\u001b[0m\n",
      "it 75/140, Jtr_KL = 6.457404, Jtr_pred = 0.143297, err = 0.039633, \u001b[31m   time: 41.727839 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086487, err = 0.024500\n",
      "\u001b[0m\n",
      "it 76/140, Jtr_KL = 6.450660, Jtr_pred = 0.141992, err = 0.040117, \u001b[31m   time: 41.564899 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087005, err = 0.024800\n",
      "\u001b[0m\n",
      "it 77/140, Jtr_KL = 6.443341, Jtr_pred = 0.139447, err = 0.038983, \u001b[31m   time: 41.377813 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085939, err = 0.024100\n",
      "\u001b[0m\n",
      "it 78/140, Jtr_KL = 6.437043, Jtr_pred = 0.139836, err = 0.039433, \u001b[31m   time: 41.515273 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080067, err = 0.024100\n",
      "\u001b[0m\n",
      "it 79/140, Jtr_KL = 6.430799, Jtr_pred = 0.140595, err = 0.040750, \u001b[31m   time: 41.495488 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086006, err = 0.023300\n",
      "\u001b[0m\n",
      "it 80/140, Jtr_KL = 6.423967, Jtr_pred = 0.141515, err = 0.039550, \u001b[31m   time: 41.525705 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081157, err = 0.021300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_best.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 81/140, Jtr_KL = 6.417614, Jtr_pred = 0.140101, err = 0.039850, \u001b[31m   time: 41.505592 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084710, err = 0.023700\n",
      "\u001b[0m\n",
      "it 82/140, Jtr_KL = 6.411369, Jtr_pred = 0.138375, err = 0.038817, \u001b[31m   time: 41.469573 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.078344, err = 0.022800\n",
      "\u001b[0m\n",
      "it 83/140, Jtr_KL = 6.405242, Jtr_pred = 0.141536, err = 0.039533, \u001b[31m   time: 41.436434 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079782, err = 0.023600\n",
      "\u001b[0m\n",
      "it 84/140, Jtr_KL = 6.398715, Jtr_pred = 0.140722, err = 0.039833, \u001b[31m   time: 41.442448 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.085741, err = 0.023600\n",
      "\u001b[0m\n",
      "it 85/140, Jtr_KL = 6.392461, Jtr_pred = 0.137860, err = 0.039683, \u001b[31m   time: 41.442458 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082169, err = 0.023800\n",
      "\u001b[0m\n",
      "it 86/140, Jtr_KL = 6.386366, Jtr_pred = 0.139223, err = 0.040300, \u001b[31m   time: 41.477425 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083314, err = 0.022600\n",
      "\u001b[0m\n",
      "it 87/140, Jtr_KL = 6.379718, Jtr_pred = 0.137680, err = 0.040283, \u001b[31m   time: 41.476706 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084955, err = 0.024700\n",
      "\u001b[0m\n",
      "it 88/140, Jtr_KL = 6.373441, Jtr_pred = 0.139087, err = 0.039667, \u001b[31m   time: 41.512907 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083066, err = 0.023600\n",
      "\u001b[0m\n",
      "it 89/140, Jtr_KL = 6.367260, Jtr_pred = 0.137109, err = 0.039933, \u001b[31m   time: 41.491908 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082708, err = 0.023000\n",
      "\u001b[0m\n",
      "it 90/140, Jtr_KL = 6.361091, Jtr_pred = 0.137221, err = 0.039950, \u001b[31m   time: 41.427125 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083240, err = 0.023800\n",
      "\u001b[0m\n",
      "it 91/140, Jtr_KL = 6.354432, Jtr_pred = 0.135127, err = 0.039350, \u001b[31m   time: 41.484180 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079596, err = 0.023700\n",
      "\u001b[0m\n",
      "it 92/140, Jtr_KL = 6.348340, Jtr_pred = 0.137693, err = 0.038750, \u001b[31m   time: 41.719079 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081085, err = 0.023000\n",
      "\u001b[0m\n",
      "it 93/140, Jtr_KL = 6.341935, Jtr_pred = 0.136329, err = 0.039617, \u001b[31m   time: 41.481369 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080906, err = 0.024100\n",
      "\u001b[0m\n",
      "it 94/140, Jtr_KL = 6.336303, Jtr_pred = 0.136393, err = 0.038683, \u001b[31m   time: 41.541280 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083636, err = 0.023400\n",
      "\u001b[0m\n",
      "it 95/140, Jtr_KL = 6.330232, Jtr_pred = 0.136596, err = 0.039700, \u001b[31m   time: 41.127989 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081870, err = 0.022300\n",
      "\u001b[0m\n",
      "it 96/140, Jtr_KL = 6.323332, Jtr_pred = 0.134460, err = 0.038967, \u001b[31m   time: 41.506139 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081863, err = 0.024600\n",
      "\u001b[0m\n",
      "it 97/140, Jtr_KL = 6.317322, Jtr_pred = 0.134119, err = 0.037700, \u001b[31m   time: 41.509178 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079946, err = 0.022900\n",
      "\u001b[0m\n",
      "it 98/140, Jtr_KL = 6.311236, Jtr_pred = 0.133247, err = 0.038950, \u001b[31m   time: 41.592317 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083149, err = 0.023800\n",
      "\u001b[0m\n",
      "it 99/140, Jtr_KL = 6.305159, Jtr_pred = 0.133676, err = 0.039217, \u001b[31m   time: 41.466165 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082007, err = 0.022500\n",
      "\u001b[0m\n",
      "it 100/140, Jtr_KL = 6.299268, Jtr_pred = 0.133950, err = 0.038733, \u001b[31m   time: 41.595933 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080448, err = 0.023300\n",
      "\u001b[0m\n",
      "it 101/140, Jtr_KL = 6.293005, Jtr_pred = 0.133527, err = 0.038467, \u001b[31m   time: 41.623771 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081048, err = 0.024100\n",
      "\u001b[0m\n",
      "it 102/140, Jtr_KL = 6.287281, Jtr_pred = 0.133455, err = 0.037400, \u001b[31m   time: 41.627449 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.076037, err = 0.021900\n",
      "\u001b[0m\n",
      "it 103/140, Jtr_KL = 6.281745, Jtr_pred = 0.131615, err = 0.037783, \u001b[31m   time: 41.653187 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079254, err = 0.022100\n",
      "\u001b[0m\n",
      "it 104/140, Jtr_KL = 6.275374, Jtr_pred = 0.132007, err = 0.037400, \u001b[31m   time: 41.629666 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079225, err = 0.022800\n",
      "\u001b[0m\n",
      "it 105/140, Jtr_KL = 6.269408, Jtr_pred = 0.134486, err = 0.039583, \u001b[31m   time: 41.622697 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084518, err = 0.022700\n",
      "\u001b[0m\n",
      "it 106/140, Jtr_KL = 6.263579, Jtr_pred = 0.133533, err = 0.036650, \u001b[31m   time: 41.625692 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.082346, err = 0.023200\n",
      "\u001b[0m\n",
      "it 107/140, Jtr_KL = 6.257882, Jtr_pred = 0.133887, err = 0.039217, \u001b[31m   time: 41.616521 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.077790, err = 0.022300\n",
      "\u001b[0m\n",
      "it 108/140, Jtr_KL = 6.252509, Jtr_pred = 0.132907, err = 0.037300, \u001b[31m   time: 41.629412 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.078279, err = 0.023200\n",
      "\u001b[0m\n",
      "it 109/140, Jtr_KL = 6.246344, Jtr_pred = 0.131580, err = 0.038300, \u001b[31m   time: 41.631173 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079535, err = 0.023500\n",
      "\u001b[0m\n",
      "it 110/140, Jtr_KL = 6.240436, Jtr_pred = 0.131131, err = 0.038033, \u001b[31m   time: 41.559982 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.090001, err = 0.024700\n",
      "\u001b[0m\n",
      "it 111/140, Jtr_KL = 6.234866, Jtr_pred = 0.130447, err = 0.037417, \u001b[31m   time: 41.555674 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.089518, err = 0.024900\n",
      "\u001b[0m\n",
      "it 112/140, Jtr_KL = 6.229021, Jtr_pred = 0.133637, err = 0.038067, \u001b[31m   time: 41.599319 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084123, err = 0.024300\n",
      "\u001b[0m\n",
      "it 113/140, Jtr_KL = 6.223112, Jtr_pred = 0.130549, err = 0.037583, \u001b[31m   time: 41.632526 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.078343, err = 0.022200\n",
      "\u001b[0m\n",
      "it 114/140, Jtr_KL = 6.218048, Jtr_pred = 0.132648, err = 0.038600, \u001b[31m   time: 41.633053 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.083214, err = 0.022800\n",
      "\u001b[0m\n",
      "it 115/140, Jtr_KL = 6.212482, Jtr_pred = 0.131018, err = 0.038800, \u001b[31m   time: 41.606886 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.074564, err = 0.022300\n",
      "\u001b[0m\n",
      "it 116/140, Jtr_KL = 6.206641, Jtr_pred = 0.130473, err = 0.038200, \u001b[31m   time: 41.536629 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086298, err = 0.024000\n",
      "\u001b[0m\n",
      "it 117/140, Jtr_KL = 6.201230, Jtr_pred = 0.130935, err = 0.038267, \u001b[31m   time: 41.604358 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080286, err = 0.022000\n",
      "\u001b[0m\n",
      "it 118/140, Jtr_KL = 6.195251, Jtr_pred = 0.130867, err = 0.037833, \u001b[31m   time: 41.778171 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.076209, err = 0.022100\n",
      "\u001b[0m\n",
      "it 119/140, Jtr_KL = 6.190018, Jtr_pred = 0.129198, err = 0.037667, \u001b[31m   time: 41.750193 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.077841, err = 0.023500\n",
      "\u001b[0m\n",
      "it 120/140, Jtr_KL = 6.184153, Jtr_pred = 0.133283, err = 0.040500, \u001b[31m   time: 41.303291 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080542, err = 0.022300\n",
      "\u001b[0m\n",
      "it 121/140, Jtr_KL = 6.178580, Jtr_pred = 0.130106, err = 0.036733, \u001b[31m   time: 41.614141 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.078289, err = 0.023700\n",
      "\u001b[0m\n",
      "it 122/140, Jtr_KL = 6.173100, Jtr_pred = 0.130300, err = 0.037850, \u001b[31m   time: 41.437715 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081150, err = 0.022400\n",
      "\u001b[0m\n",
      "it 123/140, Jtr_KL = 6.167507, Jtr_pred = 0.129589, err = 0.037850, \u001b[31m   time: 41.583656 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079901, err = 0.021900\n",
      "\u001b[0m\n",
      "it 124/140, Jtr_KL = 6.162720, Jtr_pred = 0.128884, err = 0.037717, \u001b[31m   time: 41.561531 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.076841, err = 0.022600\n",
      "\u001b[0m\n",
      "it 125/140, Jtr_KL = 6.157233, Jtr_pred = 0.129569, err = 0.037617, \u001b[31m   time: 41.612895 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079463, err = 0.023500\n",
      "\u001b[0m\n",
      "it 126/140, Jtr_KL = 6.151171, Jtr_pred = 0.131118, err = 0.037433, \u001b[31m   time: 41.507137 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.088177, err = 0.026000\n",
      "\u001b[0m\n",
      "it 127/140, Jtr_KL = 6.146221, Jtr_pred = 0.129771, err = 0.037850, \u001b[31m   time: 41.749518 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080556, err = 0.023100\n",
      "\u001b[0m\n",
      "it 128/140, Jtr_KL = 6.139941, Jtr_pred = 0.128373, err = 0.037833, \u001b[31m   time: 41.705569 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.077095, err = 0.021900\n",
      "\u001b[0m\n",
      "it 129/140, Jtr_KL = 6.134681, Jtr_pred = 0.127847, err = 0.036617, \u001b[31m   time: 41.301220 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.087076, err = 0.024700\n",
      "\u001b[0m\n",
      "it 130/140, Jtr_KL = 6.129211, Jtr_pred = 0.128319, err = 0.037383, \u001b[31m   time: 41.590061 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.080503, err = 0.022500\n",
      "\u001b[0m\n",
      "it 131/140, Jtr_KL = 6.124317, Jtr_pred = 0.129107, err = 0.037233, \u001b[31m   time: 41.543665 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.076393, err = 0.022900\n",
      "\u001b[0m\n",
      "it 132/140, Jtr_KL = 6.118260, Jtr_pred = 0.129854, err = 0.038267, \u001b[31m   time: 41.600103 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.079690, err = 0.023400\n",
      "\u001b[0m\n",
      "it 133/140, Jtr_KL = 6.113490, Jtr_pred = 0.129160, err = 0.037667, \u001b[31m   time: 41.533977 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081431, err = 0.022500\n",
      "\u001b[0m\n",
      "it 134/140, Jtr_KL = 6.108253, Jtr_pred = 0.127213, err = 0.037817, \u001b[31m   time: 41.644286 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.077870, err = 0.022500\n",
      "\u001b[0m\n",
      "it 135/140, Jtr_KL = 6.102890, Jtr_pred = 0.127087, err = 0.036817, \u001b[31m   time: 41.675665 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.073596, err = 0.021900\n",
      "\u001b[0m\n",
      "it 136/140, Jtr_KL = 6.097787, Jtr_pred = 0.125404, err = 0.037033, \u001b[31m   time: 41.703239 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.084266, err = 0.023700\n",
      "\u001b[0m\n",
      "it 137/140, Jtr_KL = 6.091942, Jtr_pred = 0.128000, err = 0.037133, \u001b[31m   time: 41.761191 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.081808, err = 0.022700\n",
      "\u001b[0m\n",
      "it 138/140, Jtr_KL = 6.087001, Jtr_pred = 0.127138, err = 0.036983, \u001b[31m   time: 41.550596 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.086116, err = 0.024500\n",
      "\u001b[0m\n",
      "it 139/140, Jtr_KL = 6.081213, Jtr_pred = 0.127990, err = 0.037317, \u001b[31m   time: 41.588907 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.091018, err = 0.025100\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 42.440565 seconds\n",
      "\u001b[0m\n",
      "\u001b[36mWritting models_weight_uncertainty_MC_MNIST_laplace/theta_last.dat\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type bayes_linear_2L. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/serialization.py:250: UserWarning: Couldn't retrieve source code for container of type BayesLinear_Normalq. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.073596 (cost_train 0.125404)\n",
      "  err_dev: 0.021300\n",
      "  nb_parameters: 4790420 (4.57MB)\n",
      "  time_per_it: 42.440565s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_MC_MNIST_laplace'\n",
    "results_dir = 'results_weight_uncertainty_MC_MNIST_laplace'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 140\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 3\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "kl_cost_train = np.zeros(nb_epochs)\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_dkl, cost_pred, err = net.fit(x, y, samples=nsamples)\n",
    "\n",
    "        err_train[i] += err\n",
    "        kl_cost_train[i] += cost_dkl\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    kl_cost_train[i] /= nb_samples\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_KL = %f, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, kl_cost_train[i], pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "\n",
    "    # Save state dict\n",
    "    \n",
    "    if i in savemodel_its:\n",
    "        save_dicts.append(copy.deepcopy(net.model.state_dict()))\n",
    "    \n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "net.save(models_dir+'/theta_last.dat')\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', kl_cost_train)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "plt.figure()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(kl_cost_train, 'r')\n",
    "ax1.set_ylabel('nats?')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax = plt.gca()\n",
    "plt.title('DKL (per sample)')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 4.79M\n",
      "\u001b[36mReading models_weight_uncertainty_MC_MNIST_laplace/theta_last.dat\n",
      "\u001b[0m\n",
      "  restoring epoch: 139, lr: 0.001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "import copy\n",
    "\n",
    "\n",
    "models_dir = 'models_weight_uncertainty_MC_MNIST_laplace'\n",
    "results_dir = 'results_weight_uncertainty_MC_MNIST_laplace'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 100\n",
    "nb_epochs = 140\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [20, 50, 80, 120]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "nsamples = 3\n",
    "########################################################################################\n",
    "net = Bayes_Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size,\n",
    "          Nbatches=(NTrainPointsMNIST/batch_size))\n",
    "\n",
    "\n",
    "net.load(models_dir+'/theta_last.dat') # theta_last.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -892.853088, err = 0.022800\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for single digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 90\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All dataset with entropy\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23928000,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800, samples: 10')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 10\n",
    "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "symlim = 0.7\n",
    "\n",
    "lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "\n",
    "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
    "ax.set_xlim((-symlim, symlim))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_state_dict = copy.deepcopy(net.model.state_dict())\n",
    "\n",
    "Nsamples = 10\n",
    "\n",
    "for idx, iteration in enumerate(savemodel_its):\n",
    "    \n",
    "    net.model.load_state_dict(save_dicts[idx])\n",
    "    weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "    fig = plt.figure(dpi=120)\n",
    "    ax = fig.add_subplot(111)\n",
    "    symlim = 1\n",
    "    lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "    sns.distplot(weight_vector, 350, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
    "    ax.legend()\n",
    "\n",
    "    ax.set_xlim((-symlim, symlim))\n",
    "\n",
    "\n",
    "net.model.load_state_dict(last_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n"
     ]
    }
   ],
   "source": [
    "print('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight SNR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) density: Total parameters: 2395210')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "SNR_vector = net.get_weight_SNR()\n",
    "np.save(results_dir+'/snr_vector_'+name+'.npy', SNR_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) density: Total parameters: %d' % (len(SNR_vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SNR (dB) CDF: Total parameters: 23928000')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "\n",
    "# mkdir('snr_weights')\n",
    "# SNR_vector = net.get_weight_SNR()\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(SNR_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(10*np.log10(SNR_vector), norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True), kde_kws=dict(cumulative=False))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('SNR (dB) CDF: Total parameters: %d' % (len(weight_vector)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# net.load(models_dir+'/theta_last.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.089264, err = 0.023000\n",
      "\u001b[0m\n",
      "-24.910207 dB\n",
      "params: 2155689\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.088467, err = 0.023000\n",
      "\u001b[0m\n",
      "-21.919030 dB\n",
      "params: 1916168\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.088317, err = 0.022700\n",
      "\u001b[0m\n",
      "-18.782174 dB\n",
      "params: 1437126\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.086966, err = 0.023000\n",
      "\u001b[0m\n",
      "-15.885066 dB\n",
      "params: 958084\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.086374, err = 0.023100\n",
      "\u001b[0m\n",
      "-12.215059 dB\n",
      "params: 479042\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.085270, err = 0.023000\n",
      "\u001b[0m\n",
      "-11.195599 dB\n",
      "params: 239521\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.084949, err = 0.023200\n",
      "\u001b[0m\n",
      "-7.072424 dB\n",
      "params: 23953\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.108170, err = 0.030200\n",
      "\u001b[0m\n",
      "-3.254470 dB\n",
      "params: 11977\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.119153, err = 0.035500\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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uD/2czU4xDZ9iGj7FNHyKae1aWlIAdHa0TXiti0tMq01I+hgf7Qjq4fDpmOJ2+G1fK2oX3A+Ac24nsBPAzH4EPIE3avKB4HFm1oJXw3IM8DvOuVKjNxXL5fIMDAxN5xQTpFJJurvbGRgYJpvNhXbeZqaYhk8xDZ9iGj7FdPoGhw4BkBnN0Nc3GGpMu7vbpz3SUm1CspmiWhEzawNOpKi2pEQ7/LZbAttX4Y2ObDmshc85lzOzX+GtORL8uUm8upK3A79bbkqnGplM+E/0bDY3I+dtZopp+BTT8Cmm4VNMazcaiFswhnGJabXpzEZgtZktCGw7F2jz95XknNuOl3SsLdp1AfBouboPfxTkdKA44fia/7M/WGoqR0RERMY1WlHrN4ArgPvM7AZgEfAl4I7gGiRmditwkXMueP5r8BZC2wY8CJwDnAWcHWh3GV7ysQl4Fa+G5HLgJP+xcNw64I+B/w0cMLPg6Mm2SW4LFhERaVq5RvpwPb9G4914a4Pcg5eMrAcuLTo05X8F224ALsFb4Ox+vGRkrXPugcBhTwPHAl/BWyztr/AWRXubc+4ngePe6z/+KfCLoq/3V3NNIiIizaCwDkm6QUZIcM5tZTwhmOyYi4GLS2y/Hbi9TLufERgxKXPc7051jIiIiIyL+5RNPMdtREREJFQZPyFJKiERERGRqORi/uF68eyViIiIhKqw1oimbERERCQyqiERERGRyCkhERERkciNTdmohkRERESiks1rhEREREQiVlgYTQmJiIiIRGashiSlhEREREQiMpaQJJSQiIiISERU1CoiIiKR022/IiIiEjnVkIiIiEjkCglJUjUkIiIiEhXVkIiIiEjkcv7CaGnVkIiIiEhUtDCaiIiIRG68qDWeL/3x7JWIiIiEJp/Pjxe1aoREREREolBIRkBTNiIiIhKRnBISERERiVpwhCSthdFEREQkCsGERDUkIiIiEonComiglVpFREQkIsEP1ksoIREREZEoxP2D9UAJiYiISMMbzXhTNvk8bHmxb8JdN3GRjroDIiIiMnMed7v5zgNbAS8x+ev1/05vVxsffa9x1tuXRdy7cUpIREREGtTjbjc33/vUYdv79o/w1e8+ydzONlYumRdBzw6nKRsREZEGlMvluXPTs2WPueW+p2IzfVP1CImZLQduAs4EBoH1wFXOueEK2l4ErAOOB54DrnPObQjs7wK+DZwGLAYOAI8B1zjnfll0rsXAV4D3ATng+8AnnHN7q70mERGRRrP1pX769o+UPWZP/zBuRx8nH9szS72aXFUjJGbWAzwMdAHnAVcCHwFuqaDt+cBtwL14ScRDwF1mdlbgsFZgGLgWWANcBnQAD/uJUOFcaeD/AG8G/gD4I+A/A/eZWXxLiEVERGZJ/2D5ZGTsuAOHZrgnlal2hORyoBc41Tm3B8DMMsAdZvZZ59zmMm1vADY459b5/37EzFYA1wMPADjn3sBLMMaY2YPAG8D5wOf8zecBvwWc4px72j9uJ/Az4L14yYqIiEjT6ulsq+y4ua0z3JPKVFtDsgbYVEhGfHcDI/6+ksxsGbACb3on6E7gdDNbWOZnDgIHgZaifjxZSEYAnHM/B14A3j/1ZYiIiDS25Ut66O0qn5Qs7GnHjuudpR6VV21CshKYMArinBsBtvn7yrWjuC3wDJDAS1bGmFnSzNJmdhTwRbwake+U60fgfOX6ISIi0hSSyQQXvufkssdces4psflsm2qnbHqB/hLb+4D5U7SjRNs+/7G47fXA1f73u4E1zrnnK+zHqjL9mFI6Hd6NR6lUcsKjTJ9iGj7FNHyKafgU09r89psWs/WlfTz42EsTts/vbuO/nr2C33nL0QwMTHlPyqyoZR2SUvcHJSbZPlXbxCTbvw58DzgKr7B1o5mtds49EVI/SkomE/T2dtbafFLd3e2hn7PZKabhU0zDp5iGTzGtXt7/7Jp3nHo0p7/pKOZ3z2HVCQtI+SMjcYlptQlJH+OjHUE9lJ5CCbbDb/taUbvgfgCcczuBnQBm9iPgCbxRkw9U0I++EtsrksvlGRgYqrX5YVKpJN3d7QwMDE/4pEWpnWIaPsU0fIpp+BTT2v1q624ATl+5iLcs8146B/YNhRrT7u72aY9eVZuQbKaoRsPM2oATgW9N0Q6/7ZbA9lV4IxpbDmvhc87lzOxXwBlF5zu1xOGrgB+W6ceUMpnwn+jZbG5GztvMFNPwKabhU0zDp5hWZ0//MHv2HSSVTHDCUd0lYxeXmFabzmwEVpvZgsC2c4E2f19JzrnteEnH2qJdFwCPFt21M4GZtQCnA8Eako3Am81sZeC4M/AWXPtRRVciIiLS4Dbv8CYNlh3VzZzWeH9aTLW9+wZwBd4CZDcAi4AvAXcE1yAxs1uBi5xzwfNfg7cQ2jbgQeAc4Czg7EC7y/CSj03Aq3g1JJcDJ/mPBXcDTwLfNbN1/nXcCPwUuL/KaxIREWlIW170EpIVS6NfiXUqVY2QOOf6gXfjrQ1yD14ysh64tOjQlP8VbLsBuARvgbP78ZKRtc65BwKHPQ0ci7ck/APAX+EtivY259xPAufK4K32+jTwj3jLzf8c+P+cc/FYlF9ERCRC+XyeLTu8G1JXxGStkXKqHr9xzm3FWw213DEXAxeX2H47cHuZdj8jMGIyxc94FfhwJceKiIg0m919w/TtHyGdSnDSMfH4RN9ydEO3iIhIAyrUj5xw9DxaW1JTHB09JSQiIiINqFA/snJp/KdrQAmJiIhIw5lYPxL/glZQQiIiItJwdr4xxMDgIVrSSU44Ov71I6CEREREpOEUpmtOOmYeLSF+RttMqo9eioiISMW27CisP1If9SOghERERKSh5PJ5nF8/Ui8FraCEREREpKG8vPsAB4ZHaWtJcfzirqi7UzElJCIiIg2kcHfNyUvmkZ7mJ/DOpvrpqYiIiExpbP2ROlguPkgJiYiISIPI5fK4l/z1R+qofgSUkIiIiDSMHbv3MzySob0tzXFHzo26O1VRQiIiItIgNvvTNbakh1Syvl7i66u3IiIiMqktL9bXcvFBSkhEREQaQCabY+vL9Vk/AkpIREREGsKLu/YzcihL55w0xy6qr/oRUEIiIiLSEMaWiz+ul2QiEXFvqqeEREREpAEUClrrcboGlJCIiIjUvdFMjude3gfUZ0ErKCERERGpe9tfHeBQJkd3RwtHL+yMujs1UUIiIiJS5wrLxdtxvSTqsH4ElJCIiIjUvbGC1jqtHwElJCIiInXt0GiW517x6kdWKiERERGRKGx7ZR+ZbJ6eua0c2dsedXdqpoRERESkjm3eMb46a73Wj4ASEhERkboWXBCtnikhERERqVMjh7Js3zkA1HdBKyghERERqVvPvtxPNpdnQfccjpg3J+ruTIsSEhERkTq1eex23566rh8BJSQiIiJ1a8uLfkFrndePAKSrbWBmy4GbgDOBQWA9cJVzbriCthcB64DjgeeA65xzG4rOfQWwGlgK7AE2AVc753YVnesdwHXAqUAOeMI/7tFqr0lERKTeDI9keHHXfqC+1x8pqGqExMx6gIeBLuA84ErgI8AtFbQ9H7gNuBd4H/AQcJeZnRU47CzgncA3gfcDV/v//oWZzQ2c603A/cBB4ELgYqAD2GRmx1dzTSIiIvVo60v95PJ5FvW0M7+7vutHoPoRksuBXuBU59weADPLAHeY2Wedc5vLtL0B2OCcW+f/+xEzWwFcDzzgb/sn4GbnXL7QyMyeBH6NlwDd7m8+13/8YGFkxsweBXbhJTt/W+V1iYiI1JXNL9b/cvFB1daQrAE2FZIR393AiL+vJDNbBqzAm94JuhM43cwWAjjn9gSTEd9vgCxwdGBbC3AIb4SkYABv6qa+q3pEREQqsCVQ0NoIqh0hWQl8K7jBOTdiZtv8feXaARSPoDyDl0CsAH46Sdu3A6mitncA/xP4vJn9Dd51XA+8Dmw47AxVSKfDq/NNpZITHmX6FNPwKabhU0zDp5hOdGB4lJdeOwDAKcsW1PTaFbeYVpuQ9AL9Jbb3AfOnaEeJtn3+Y8m2ZtYCfBlwwA8L251zW81sNXAf8Cl/807g95xzr5e7gHKSyQS9vZ21Np9Ud3f9frZAXCmm4VNMw6eYhk8x9Wx5+VXywLGL5rLsuHIvv1OLS0yrvssGKJ5SAW+Uo9T2qdomJtle8DXgFOAdzrlMYaN/N87deIWxt+FN4fwPYKOZ/Sfn3I4K+nKYXC7PwMBQLU1LSqWSdHe3MzAwTDabC+28zUwxDZ9iGj7FNHyK6USPPf0qAMuX9NDXN1jTOcKMaXd3+7RHWqpNSPoYH+0I6uHw6ZjidvhtXytqF9w/xsw+A/whXuHqY0W7P+ef56OFmhMzexh4Hu/Onz8pfxmTy2TCf6Jns7kZOW8zU0zDp5iGTzENn2LqeeaFvQCsWNIz7XjEJabVpjObKaoVMbM24ETKJySFfcV1JqvwRke2FJ3zY8C1wMecc98vcb5VwK+DBbDOuYN4UzsnTnkVIiIidWpg6BAvv+6NithxjVHQCtUnJBuB1Wa2ILDtXKDN31eSc247XtKxtmjXBcCjwbt2zOz3ga8C1zjnvjnJKV8E3mpmiUC7DrxE5YWKr0ZERKTOuB1eOeaxR3TS1dEacW/CU+2UzTfwVlK9z8xuABYBXwLuCK5BYma3Ahc554LnvwZvIbRtwIPAOXgLoZ0daPdO4B+AnwAPmtkZgfavO+e2+d9/Hfg+sN7MbgNagU/gTQl9o8prEhERqRtbCuuPNMBy8UFVjZA45/qBd+MtGX8PXjKyHri06NCU/xVsuwG4BDgfb5XVs4C1zrkHAoe9C69A9Z3AL4q+Ph041w/wFko7Hm8xtcKtyKudc09Wc00iIiL1ZHz9kcZKSBL5fCU3xzSF57PZ3LK9e2urVi4lnU7S29tJX99gLAqGGoFiGj7FNHyKafgUU0//gRH+7Gs/IwHc9Ikz6ZzTUvO5wozp/PmdpFLJ7cAJtZ4jHquhiIiIyJQK0zXHHdk1rWQkjpSQiIiI1IlGWy4+SAmJiIhIndjyoneHTaMVtIISEhERkbqwd+Agu/uHSSYSLF+iERIRERGJwGa/fmTp4i7a22r55Jd4U0IiIiJSBwoFrSsb7HbfAiUkIiIiMZfP5xu6oBWUkIiIiMTe6/sO8sbACKlkgpOPUUIiIiIiEShM1yw7upu21tQUR9cnJSQiIiIxNzZd04C3+xYoIREREYmxfD4/dodNoxa0ghISERGRWNu1d4h9Bw6RTiU56ZjuqLszY5SQiIiIxNiWHd7qrCcd001LujHrR0AJiYiISKwVClobuX4ElJCIiIjE1sT1R5SQiIiISARe2TPI/qFRWtNJTji6cetHQAmJiIhIbBWma04+dh7pVGO/ZDf21YmIiNSxQkFro0/XgBISERGRWMrl87gmWBCtQAmJiIhIDL28+wCDBzO0taZYurgr6u7MOCUkIiIiMVRYndWW9DR8/QgoIREREYmlZll/pEAJiYiISMxkczm2vlwoaO2JuDezQwmJiIhIzOx47QDDI1k62tIct6jx60dACYmIiEjsFKZrli/pIZlMRNyb2aGEREREJGYKBa0rm2D9kQIlJCIiIjGSyeZ49uV9QHMsiFaghERERCRGXnh1PyOjWea2t3DMEZ1Rd2fWKCERERGJkc3+6qx2XA/JRHPUjwCkq21gZsuBm4AzgUFgPXCVc264grYXAeuA44HngOuccxuKzn0FsBpYCuwBNgFXO+d2lTjfOf753gIcBB4H1jrn9lZ7XSIiInHQbOuPFFQ1QmJmPcDDQBdwHnAl8BHglgrang/cBtwLvA94CLjLzM4KHHYW8E7gm8D7gav9f//CzOYWne9i4C68hOX9wEXAk0BrNdckIiISF6OZHM+90nz1I1D9CMnlQC9wqnNuD4CZZYA7zOyzzrnNZdreAGxwzq3z//2Ima0Argce8Lf9E3Czcy5faGRmTwK/xkuAbve3LcAbpfkT59w3Az/jB1Vej4iISGw8v3Mfo5kc3Z2tHL2gI+ruzKpqa0jWAJsKyYjvbmDE31eSmS0DVuBN7wTdCZxuZgsBnHN7gsmI7zdAFjg6sO3DQAJvxEVERKQhbB6brukh0URSLr7ZAAAY2ElEQVT1I1B9QrISmDAK4pwbAbb5+8q1o7gt8AxeYrGiTNu3A6mitmcADrjYzHaY2aiZPW5m7576EkREROJpy47CcvHNNV0D1U/Z9AL9Jbb3AfOnaEeJtn3+Y8m2ZtYCfBkv+fhhYNdiwIBrgU8Bu4E/BTaa2Srn3PNl+lJWOh3ejUcp/9MZU03wKY2zRTENn2IaPsU0fM0Q05HRLM/v9OpHTjlhQaivR6XELaZV32UDFE+pgDfKUWr7VG0Tk2wv+BpwCvAO51wmsD0JzMW7o2YjgJn9K7Adr9D2YxX05TDJZILe3vDv+e7ubg/9nM1OMQ2fYho+xTR8jRzTX23dTSabZ8G8Oaw4YeGsTdnEJabVJiR9jI92BPVw+HRMcTv8tq8VtQvuH2NmnwH+EPigc+6xot2F23ofKWxwzg2b2b8BbyrTj7JyuTwDA0O1Nj9MKpWku7udgYFhstlcaOdtZopp+BTT8Cmm4WuGmD761KuAVz/S3x/ea9Fkwoxpd3f7tEdaqk1INlNUK2JmbcCJwLemaIffdktg+yq80ZHgNszsY3jTMZc7574/yfkmG6mZVlQzmfCf6NlsbkbO28wU0/AppuFTTMPXyDF95gXvvfbyJT2zeo1xiWm16cxGYLV/223BuUCbv68k59x2vKRjbdGuC4BHg3ftmNnvA18Frim6pTfoh3jJx+pAuw68AthfV3w1IiIiMXDwUIYXXt0PwMomWxCtoNoRkm/graR6n5ndACwCvgTcEVyDxMxuBS5yzgXPfw3eQmjbgAeBc/AWQjs70O6dwD8APwEeNLMzAu1fd85tA3DOPWZm9wF/b2ZXMV7U2gHcWOU1iYiIROrZl/eRzeVZOG8OC3viUdMx26oaIXHO9QPvxlsy/h68ZGQ9cGnRoSn/K9h2A3AJcD5wP14ystY590DgsHcBLfirsxZ9fbroZ3wUbw2UG/2+tAGrnXOvVHNNIiIiUWvW5eKDEvl8JTfHNIXns9ncsr17B0M7YTqdpLe3k76+wVjMzzUCxTR8imn4FNPwNXpMr7/tl7ywaz+XfmAVbz9l8az8zDBjOn9+J6lUcjtwQq3niMfNxyIiIk1q6GCGF1/z6keacUG0AiUkIiIiEdr6Uj/5PBzZ205vV1vU3YmMEhIREZEIbdnh14808egIKCERERGJlApaPUpIREREInJgeJQduw8AGiFRQiIiIhIR50/XHL2wk3mdrRH3JlpKSERERCKy5cV+wPv8mmanhERERCQiYwWtTV4/AkpIREREIrFv8BCv7PEW4zSNkCghERERiUKhfmTJorl0dTR3/QgoIREREYmEbvedSAmJiIhIBDbv8Atal2q6BpSQiIiIzLq+/SO8tneIRAJsiRISUEIiIiIy6wp31xx3ZBcdc1oi7k08KCERERGZZZv9+pGVqh8Zo4RERERklo0VtDb5cvFBSkhERERm0Z59w+zZd5BkIsHJx86LujuxoYRERERkFhWWi192VBftbemIexMfSkhERERm0dhy8ZqumUAJiYiIyCzJ5/NjBa1aEG0iJSQiIiKzZHf/MH37R0glE5yk+pEJlJCIiIjMksLdNSce3U1bSyri3sSLEhIREZFZsmVsuXhN1xRTQiIiIjIL8vm8PlCvDCUkIiIis+DVN4bYN3iIdCrJicd0R92d2FFCIiIiMgsKt/uefOw8WtKqHymmhERERGQWjE/X6NN9S1FCIiIiMsNy+bwKWqeghERERGSG7Xx9kAPDo7S2JFl2lOpHSlFCIiIiMsMKq7OefGwP6ZReekup+lN9zGw5cBNwJjAIrAeucs4NV9D2ImAdcDzwHHCdc25D0bmvAFYDS4E9wCbgaufcrknOmQQeA94KfMg5991qr0lERGQmFQpaV2q6ZlJVpWlm1gM8DHQB5wFXAh8Bbqmg7fnAbcC9wPuAh4C7zOyswGFnAe8Evgm8H7ja//cvzGzuJKe+HDi6musQERGZLblcHleoH9H6I5OqdoTkcqAXONU5twfAzDLAHWb2Wefc5jJtbwA2OOfW+f9+xMxWANcDD/jb/gm42TmXLzQysyeBX+MlQLcHT2hmC4G/BD4J3FrltYiIiMy4l3YfYGgkw5zWFEsXT/beWqqdyFoDbCokI767gRF/X0lmtgxYgTe9E3QncLqfWOCc2xNMRny/AbKUHgX5PPAI3qiNiIhI7BTqR5Yv6SGVVP3IZKqNzEpgwiiIc24E2ObvK9eO4rbAM0ACL1mZzNuBVHFbM3sbcCHetJGIiEgsFepHNF1TXrVTNr1Af4ntfcD8KdpRom2f/1iyrZm1AF8GHPDDwPYkcDPwRefcC2Z2/JQ9r1A6HV72mvIrqVOqqA6NYho+xTR8imn46jWm2VyOrS95L32nnDg/1NeY6YpbTKu+ywYonlIBb5Sj1Pap2ibKnBPga8ApwDucc5nA9j8CjgK+UMHPrFgymaC3tzPMUwLQ3d0e+jmbnWIaPsU0fIpp+Ootpu7FvRw8lGVuewu/ZYtJJhNTN5plcYlptQlJH+OjHUE9HD4dU9wOv+1rRe2C+8eY2WeAPwQ+6Jx7LLB9LvA5vDtwWs2sFSisMtNhZt3OuYEKruUwuVyegYGhWpqWlEol6e5uZ2BgmGw2F9p5m5liGj7FNHyKafjqNab/9zevAmDH9bBvX3ivL2EIM6bd3e3THmmpNiHZTFGtiJm1AScC35qiHX7bLYHtq/BGR4LbMLOPAdcClzvnvl90roXAAuDv/K+g2/ESnsVTXMekMpnwn+jZbG5GztvMFNPwKabhU0zDV28xfeaFvYBX0BrXfsclptWmMxuB1Wa2ILDtXKDN31eSc247XtKxtmjXBcCjwbt2zOz3ga8C1zjnvlnidLuAdxV9XeDvuxb4YBXXIyIiMiMy2RzPvuzVj6xUQeuUqh0h+QbeSqr3mdkNwCLgS8AdwTVIzOxW4CLnXPD81+AthLYNeBA4B28htLMD7d4J/APwE+BBMzsj0P5159w259xB4MfBTgWKWp92zv28ymsSEREJ3fM7Bzg0mmNuewtHHxF+fWKjqWqExDnXD7wbb8n4e/CSkfXApUWHpvyvYNsNwCXA+cD9eMnIWufcA4HD3gW04K/OWvT16Wr6KiIiEqWx232X9pJMxK+YNW6qvsvGObcVeO8Ux1wMXFxi++0UrbZatP9avGmXavv0AuN37IiIiERui78g2srjeqY4UkCf9isiIhK60UyW517xbvhcoQ/Uq4gSEhERkZBte2WATDbHvM5WFs/viLo7dUEJiYiISMgKn1+zYmkvCdWPVEQJiYiISMgKBa0rNV1TMSUkIiIiIRoZzfL8Tr9+RAWtFVNCIiIiEpJcLs8jT7xMNpenq6OFBd1zou5S3ajlw/VERESkyONuN3duepa+/SMA7B8a5c//7hdc+J6TOc0WRdy7+NMIiYhIiHK5PJtf2Mu/PPEym1/YSy5XyQehS7173O3m5nufGktGCvr2j3DzvU/xuNsdUc/qh0ZIRERCUvwOGaC3q03vkBtcLpfnzk3Plj1m/aZneevJR5BM6o6byWiEREQkBHqH3Fxy+TwDQ4fY8dp+/vn/7jjs/73Y3v0jbH2pf5Z6V580QjJDCsO2o9v7aEnkOfHoebHNjHO5PFtf6qd/cISezjaWL+mJbV8lXPX0PI0zvUOeWbP5PM3n8wwezNB/YMT72n9o/PsD3vf7/O+zVU7H9Q+WT1qanRKSGVBPw7b11FcJl/7vPfl8nkw2Tyab87/yZLM5RrM5stl80ePE7Rn/+5dfP1DRO+S7Hn6WY4+YSzqVJJVK0JJKkkolaUklvMd0klQy4T2mkv5+77i0f0yzfUhbWM/TfD7P8EiGPj+p6N8/wr7BQ/Tvn5hs9B84RCabq/i83R0tzGlNsbv/4JTH9nS2VXzeZpTI51Vw5Xs+m80t27t3cFonKQzbTubj554Smz/29dTXgnQ6SW9vJ319g2Qylf/RkIlm8/8+n8+Ty+fJZPJkcjkymdxhCUCo32dyZHJ5/zF4TGBb4Jhq3+VGLZVMlE5mJkty/O3pVNL/KvN9Okk6mSSdTniPqcD36SnaprxkKsxVSSt9ng6PZIqSivGRjX2B7Yeq+Jsxt72Fnrmt9Mxt8766WpnXOf5979w2ujtbSaeS5HJ5Pvm3Py+blM7vauOv//h3YjVCFubf0/nzO0mlktuBE2ruz7R6IBNUMmz77X/ewr6hQ5G/y8nl89zz4+fLHqMh5saTz+cZOpjhHx/YWva4W3+0mede2Uc2Nz564I0OlBo9yDGayZPN5Rj1X+C9x/EkoJ5e8pOJRFUvwulUkqGDo2zZMXV9wPJj59Help6YME2RqBUnTNlcnmwuz6HReCbkE2NTZSIU2J5KwUOPv1L2Z/3t954inUpWlWh0zknTM7eNecFko/B9l/f9vM42WtKVl1gmkwkufM/JZZOnC95zsv6WTkEjJOOmPUKy5cU+/nr9v4fYpeidvnIRbzp+PosXdLB4fgddHa2R9kcjJONyfnIxODzKgeFR9g+PMjg8yv6hUQYPetsODPmPga84jAikU0la0glSyaJpiqS/PZUknQy+Yy//glYYFSj9QjhJ23TRz/C31/KiMZPvkHP5PNkwR5EqTYTKJaCB7XF5CWlvSx2eYAQTj642ejpbaW1JzVgfSk0vze9q44KYToNqhKSBVVqwtHRxF/O7op1L3Lt/hBd37Z/yuEc37+bRzeN3B3TOSbN4vpecFJKUI+d3cGRvOy3pmftFb3TZXI7B4cxhycOEr6FRDhycmHTM5IvBW06cz5JFXUXJQnBaIDg9MMkQftExqVQi9GH9OJjJd8jJRIJkOlHVO/bZlMuNJyrBxKbS0bSS02wZry7nqe17p/z5a1efxO/+1jG0tUb/9+c0W8RbTz5CNwnUSAlJiCotWFr7rpNYEfEHLlU6mvMfTl7IyGiWXXuHeGNghMGDGbbtHGCb/zkNBQlgwbw5Y0lK8Ku3qy2UF6B6uSMkk81NOjoxWaIxNJKp+ee1taboam+hs72FrvYW5ga+72xvoavDe5w7x/v+lT2D/O///9dTnvfs05dG/jytJ6fZIj5+7il19Q45DMlkgrZkCkIeedjyYl9FCcnSRV2xSEYKksmEfm9qpIQkRMuX9NDb1TblsO3yJdF/2FKlff3YuW8ee9EfGc3y2t4hXusbZtcbg+zaOzT2NTySZc++g+zZd5Cnnp/4R6S1Jcni3o6JycqCDo7s7aC9rbKnYFR3hIyMZsdGJA4cHE8yBgNTJBOmS4ZHGTmUrfnndbSlmdvhJRVTfnW00Dmnpep3zj1z2+rmeVpvCu+Qt+3cx2g+EevEOe7q6e+phEM1JON0l02RSvuaz+cZGBqdkKS8tneYV/cO8XrfMLkyz7F5c1s5av741E8hWVk4bw6pZDK0fubzeQ4eypaeBikzejFa47xqIgGdcyaOThQSjcLIxeHJRXrsmmdaPT1P65FqncKh5+nMilsNiRKScaEkJFBfhU0z3ddMNsfr/cNjScquvYPsesNLWgaGRidtl0omWNTbzpG97WzZ0c/BMqMOc9tb+NC7TmRwODMjxZypZGIsaZg7ZzyBCCYUwemSuR0ttLelI7+Tair19DytN0pIwqPn6cxRQhJfoSUk4NU71MuwbVQrtQ4dHGVXIUnZOzSWqLzWN1zzyMRUWtLJw0cpOiaOYBR/zWlNNVwRZkE9PU/riRKScOl5OjOUkMRXqAkJ6I9SrXL5PHsHDrJr7xD/9vRr/PypXVO2OfaITo49Yu7EEYuOwws922bwlr96pedp+BTT8Cmm4YtbQqKiVomdZCLBwnntLJzXTjqZrCghufA9y1XZLiJSx+J5Y7uIr1BpX44q7UVE6p8SEom1woJT5WhJZhGR+qeERGKvsOBU8UjJ/K423fYnItIgVEMidUELTomINDYlJFI3kskEK4+fr0p7EZEGpCkbERERiZwSEhEREYmcEhIRERGJnBISERERiZwSEhEREYmcEhIRERGJnD5cb9xwPp+fk6vxI+onk0olyWZ1e2qYFNPwKabhU0zDp5iGL6yYJpMJEonEQaC91nMoIRnXD7QBr0bdERERkTpzFDAC1PzBYkpIREREJHKqIREREZHIKSERERGRyCkhERERkcgpIREREZHIKSERERGRyCkhERERkcgpIREREZHIKSERERGRyCkhERERkcgpIREREZHIKSERERGRyKWj7kC9MrPlwE3AmcAgsB64yjk3XEHbi4B1wPHAc8B1zrkNM9fb+lBLTM2sG/gz4H2AAaPA48BfOOeemPFOx9x0nqeBc5wL3AM87Zw7ZUY6Wkem+bs/H/hL4FygF9gBfNE5942Z63H81RpTM+sEPg18CO/D3V4B7gA+75wbmdFOx5yZnQRcCZwBnAJsqfT318yuBP47sBj4DfBJ59yPZ6irYzRCUgMz6wEeBrqA8/D+0z8C3FJB2/OB24B78V5EHwLuMrOzZqq/9WAaMT0OuBzYBKwFLgFSwM/N7D/MWIfrwHSep4FztANfAl6biT7Wm2n+7s8F/gX4j8D/AM4GbsR7vjataT5P/xb4GPBl4P3A3wN/gRfXZvcmvJg8BzxTaSM/Gfkc8DVgjd/+n83szTPRySCNkNTmcrx3N6c65/YAmFkGuMPMPuuc21ym7Q3ABufcOv/fj5jZCuB64IGZ7HTM1RrT7cCJzrmhwgYz2wQ8D1yBl6A0q+k8TwvW4b2L3473QtrsphPTvwDagdMD7/x/PJOdrRM1xdTM0ngjI3/tnPuqv/kRM1uK9+bkT2a+67H2A+fcfQBmdhsV/P6aWRvwv4AvO+f+xt/2L3ijJFcDvz9jvUUjJLVaA2wq/PL47gZG/H0lmdkyYAXecGTQncDpZrYw7I7WkZpi6pwbDCYj/raDwGbg6JnoaB2pKaYFZnYi8D/RH/ag6cT0vwG3VjNd1iRqjWkC7031vqLt/f6+puacy9XQ7HeAeQReo5xzWeAuYI2ZzWhclZDUZiXeC94Yf75ym7+vXDuK2+INpyXwkpVmVWtMD+PPK7+1+HxNaLox/QrwD865X89A3+pVTTH134wcCfSZ2Q/NbMTM3jCzm/1psWZWU0ydc6PAt4ErzOy3zWyumb0LuBRvukGqV4j3lqLtz+BNqR0zkz9cUza16cXLwov1AfOnaEeJtn3+Y7m2ja7WmJbyl0AH+qNUc0zN7L/gvVtaPgP9qme1xnSx/3gjsAHvnf8q4PNAK96LaLOazu/+HwN/B/xbYNtXnXPXh9S3ZtMLjJQYxQu+Rr08Uz9cCUnt8iW2JSbZPlXbxCTbm810YgqAmV0IfAL4uHPuubA6VseqjqmZzcErEvxM0TC6eGp5nhZGozc75/6b//1DZtYC3Ghmn3bO7Qqzk3Wm1t/9LwAfAC4DHHAacJ2Z9TnnPhNuF5vGZP8Xk+0LjRKS2vQxPtoR1EP5aYJCltnLxLsWeor2N6NaYzrGzH4Pbwj3Rufc10PsW72qNaafAHLAev8OCPDexSf9fw855w6F2tP6UWtM9/qPDxdtfxgvWVkJNGtCUlNMzewUvDtyznHOfd/f/K9mlgP+xsxuds7tDr23ja0PmGNmc/xavIJZeY1SDUltNlM0t+lXJ59I+T9KhX3F86Kr8DLP4nm7ZlJrTAvHno63VsYG4FMz0cE6VGtMVwAnAa/j/QHqAy7wz9WHV5zZrGqN6TagVBJXeOdZSwFio6g1pqv8x18Vbf8V3pvtpWF1sImUe43aj7fOy4xRQlKbjcBqM1sQ2HYu0ObvK8k5tx0v6VhbtOsC4NEmHx6vKaYAZrbSP+ZnwCXOuWaf+iqoNaZfAN5V9HU/8IL//fcnbdn4av3dPwQ8CKwu2rUayFDFOhENqNbn6Yv+42lF2wu3t74QSu+ay8/x7loae40ysxTwYWDjTP9tTeTz+ttdLX/Y+im8J/wNwCK8xaPud859NHDcrcBFzrl0YNuH8G6h+jzeH6hz8BdJcs417ToktcbUzBYBjwEtwEfxVnksGHHO/fusXEAMTed5WuJctwH/sdlXap3m7/7pwE+BfwL+Ee9d52eBbzrn/nS2riFupvG7n8J7AT0e+AxeDcnbgGuAHzrnZnTNjLgzsw7Gb5v+ON6I05/5//4X59zrZvYQsNQ5d1KgXWFhtHXAE8AfAR/EWz/nNzPZZ42Q1MA51w+8G+/F7x68X571HF4pn6JoFUZ/ifhLgPPx3nWeBaxt5mQEphXTVcASvLsYNgG/CHzdO7O9jrfpPE+ltGn+7j+Kt3LmKuAHwJ8DX/Ufm1atMfXXx/gvwPfwpmk34r14ftV/bHaL8KawNwC/i/d3svDvN/nHpDi8lvSLeIug/Qnwz3h32q2Z6WQENEIiIiIiMaAREhEREYmcEhIRERGJnBISERERiZwSEhEREYmcEhIRERGJnBISERERiZwSEhEREYmcEhIRERGJnBISERERiZwSEhEREYmcEhIRERGJnBISERERidz/A3eraJr+xXtxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_snr = np.percentile(SNR_vector, p*100)\n",
    "        print('%f dB' % (10*np.log10(min_snr)))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=0, thresh=min_snr)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/snr_proportions', delete_proportions)\n",
    "np.save(results_dir+'/snr_prune_err', err_vec)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight KLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "np.save(results_dir+'/kld_vector_'+name+'.npy', KLD_vector)\n",
    "\n",
    "print(results_dir+'/kld_vector_'+name+'.npy')\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('KLD density: Total parameters: %d, Nsamples: %d' % (len(KLD_vector), Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2395210,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'KLD CDF: Total parameters: 23928000, Nsamples: 20')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'Bayes_By_Backprop'\n",
    "# Nsamples = 20\n",
    "# mkdir('snr_weights')\n",
    "# KLD_vector = net.get_weight_KLD(Nsamples)\n",
    "# np.save('weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(KLD_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(KLD_vector, norm_hist=False, label=name, ax=ax, hist_kws=dict(cumulative=True))\n",
    "# ax.set_xlim((-2, 2))\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Cumulative Density')\n",
    "ax.legend()\n",
    "plt.title('KLD CDF: Total parameters: %d, Nsamples: %d' % (len(weight_vector), Nsamples))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluate pruning performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]\n",
      "-inf\n",
      "params: 2395210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delete proportion: 0.000000\n",
      "\u001b[34m    Jtest = 0.089772, err = 0.023400\n",
      "\u001b[0m\n",
      "0.017055 nats\n",
      "params: 2155348\n",
      "Delete proportion: 0.100000\n",
      "\u001b[34m    Jtest = 0.089415, err = 0.023300\n",
      "\u001b[0m\n",
      "0.054758 nats\n",
      "params: 1915932\n",
      "Delete proportion: 0.200000\n",
      "\u001b[34m    Jtest = 0.089603, err = 0.024200\n",
      "\u001b[0m\n",
      "0.106967 nats\n",
      "params: 1436910\n",
      "Delete proportion: 0.400000\n",
      "\u001b[34m    Jtest = 0.093398, err = 0.025900\n",
      "\u001b[0m\n",
      "0.162644 nats\n",
      "params: 956792\n",
      "Delete proportion: 0.600000\n",
      "\u001b[34m    Jtest = 0.102571, err = 0.029500\n",
      "\u001b[0m\n",
      "0.247930 nats\n",
      "params: 479562\n",
      "Delete proportion: 0.800000\n",
      "\u001b[34m    Jtest = 0.111871, err = 0.033400\n",
      "\u001b[0m\n",
      "0.310748 nats\n",
      "params: 239354\n",
      "Delete proportion: 0.900000\n",
      "\u001b[34m    Jtest = 0.125057, err = 0.037100\n",
      "\u001b[0m\n",
      "0.427574 nats\n",
      "params: 23746\n",
      "Delete proportion: 0.990000\n",
      "\u001b[34m    Jtest = 0.144800, err = 0.042200\n",
      "\u001b[0m\n",
      "0.453450 nats\n",
      "params: 11890\n",
      "Delete proportion: 0.995000\n",
      "\u001b[34m    Jtest = 0.160254, err = 0.045800\n",
      "\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delete_proportions = [0, 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 0.99, 0.995]# 0.9999, 0.99999, 0.999999, 0.9999999, 0.99999999] # \n",
    "# keep_proportions = 1 - delete_proportions\n",
    "\n",
    "\n",
    "batch_size = 256\n",
    "Nsamples_predict = 100\n",
    "# Nsamples_KLD = 20\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "err_vec = np.zeros(len(delete_proportions))\n",
    "\n",
    "print(delete_proportions)\n",
    "\n",
    "for idx, p in enumerate(delete_proportions):\n",
    "    if p > 0:\n",
    "        min_kld = np.percentile(KLD_vector, p*100)\n",
    "        print('%f nats' % (min_kld))\n",
    "        og_state_dict, n_unmask = net.mask_model(Nsamples=20, thresh=min_kld)\n",
    "#         print(net.model.state_dict()) # for debug purposes\n",
    "        print('params: %d' % (n_unmask))\n",
    "    else:\n",
    "        print('-inf')\n",
    "        print('params: %d' % (net.get_nb_parameters()/2))\n",
    "    \n",
    "    test_cost = 0  # Note that these are per sample\n",
    "    test_err = 0\n",
    "    nb_samples = 0\n",
    "    for j, (x, y) in enumerate(valloader):\n",
    "        cost, err, probs = net.sample_eval(x, y, Nsamples_predict, logits=False) # , logits=True\n",
    "\n",
    "        test_cost += cost\n",
    "        test_err += err.cpu().numpy()\n",
    "        test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    test_cost /= nb_samples\n",
    "    test_err /= nb_samples\n",
    "\n",
    "    print('Delete proportion: %f' % (p))\n",
    "    cprint('b', '    Jtest = %1.6f, err = %1.6f\\n' % (test_cost, test_err))\n",
    "    err_vec[idx] = test_err\n",
    "    \n",
    "    if p > 0:\n",
    "        net.model.load_state_dict(og_state_dict)\n",
    "    \n",
    "    \n",
    "plt.figure(dpi=100)\n",
    "plt.plot(delete_proportions, err_vec, 'o-')\n",
    "\n",
    "np.save(results_dir+'/kld_proportions', delete_proportions)\n",
    "np.save(results_dir+'/kld_prune_err', err_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "source": []
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   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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